





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class   A							(						__lowercase		,   __lowercase		,   __lowercase		,   unittest.TestCase    ):
      _snake_case				  =StableDiffusionInstructPixaPixPipeline
      _snake_case				  =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
      _snake_case				  =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _snake_case				  =IMAGE_TO_IMAGE_IMAGE_PARAMS
      _snake_case				  =IMAGE_TO_IMAGE_IMAGE_PARAMS



      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     int:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =UNetaDConditionModel(
             block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=8		,	out_channels=4		,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,	cross_attention_dim=32		,	)
         UpperCAmelCase_           =PNDMScheduler(skip_prk_steps=_lowerCAmelCase							)
         torch.manual_seed(0							)
         UpperCAmelCase_           =AutoencoderKL(
             block_out_channels=[32, 64]		,	in_channels=3		,	out_channels=3		,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,	latent_channels=4		,	)
         torch.manual_seed(0							)
         UpperCAmelCase_           =CLIPTextConfig(
             bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1e-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1000		,	)
         UpperCAmelCase_           =CLIPTextModel(_lowerCAmelCase							)
         UpperCAmelCase_           =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

         UpperCAmelCase_           ={
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    Tuple=0							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =floats_tensor((1, 3, 32, 32)		,	rng=random.Random(_lowerCAmelCase							)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =image.cpu().permute(0		,	2		,	3		,	1							)[0]
         UpperCAmelCase_           =Image.fromarray(np.uinta(_lowerCAmelCase							)							).convert("RGB"							)
         if str(_lowerCAmelCase							).startswith("mps"							):
            UpperCAmelCase_           =torch.manual_seed(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "image_guidance_scale": 1,
             "output_type": "numpy",
         }
         return inputs



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =self.get_dummy_components()
         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase							)
         UpperCAmelCase_           =sd_pipe.to(_lowerCAmelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs(_lowerCAmelCase							)
         UpperCAmelCase_           =sd_pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase_           =np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =self.get_dummy_components()
         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase							)
         UpperCAmelCase_           =sd_pipe.to(_lowerCAmelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs(_lowerCAmelCase							)
         UpperCAmelCase_           ="french fries"
         UpperCAmelCase_           =sd_pipe(**_lowerCAmelCase		,	negative_prompt=_lowerCAmelCase							)
         UpperCAmelCase_           =output.images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase_           =np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =self.get_dummy_components()
         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase							)
         UpperCAmelCase_           =sd_pipe.to(_lowerCAmelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs(_lowerCAmelCase							)
         UpperCAmelCase_           =[inputs["prompt"]] * 2

         UpperCAmelCase_           =np.array(inputs["image"]							).astype(np.floataa							) / 2_55.0
         UpperCAmelCase_           =torch.from_numpy(_lowerCAmelCase							).unsqueeze(0							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =image / 2 + 0.5
         UpperCAmelCase_           =image.permute(0		,	3		,	1		,	2							)
         UpperCAmelCase_           =image.repeat(2		,	1		,	1		,	1							)

         UpperCAmelCase_           =sd_pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         UpperCAmelCase_           =np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =self.get_dummy_components()
         UpperCAmelCase_           =EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85		,	beta_end=0.0_12		,	beta_schedule="scaled_linear"							)
         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase							)
         UpperCAmelCase_           =sd_pipe.to(_lowerCAmelCase							)
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =self.get_dummy_inputs(_lowerCAmelCase							)
         UpperCAmelCase_           =sd_pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1]

         UpperCAmelCase_           =[round(_lowerCAmelCase		,	4							) for x in image_slice.flatten().tolist()]
         print(",".join([str(_lowerCAmelCase							) for x in slice]							)							)

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase_           =np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[Any]:
         '''simple docstring'''

         super().test_inference_batch_single_identical(expected_max_diff=3e-3							)






      def 						lowerCAmelCase__     (						self:    str							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_dummy_components()
         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase							)
         UpperCAmelCase_           =VaeImageProcessor(do_resize=_lowerCAmelCase		,	do_normalize=_lowerCAmelCase							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =pipe(**self.get_dummy_inputs_by_type(_lowerCAmelCase		,	input_image_type="pt"							)							)[0]

         UpperCAmelCase_           =components["vae"]
         UpperCAmelCase_           =self.get_dummy_inputs_by_type(_lowerCAmelCase		,	input_image_type="pt"							)

         for image_param in self.image_latents_params:
            if image_param in inputs.keys():
               UpperCAmelCase_           =vae.encode(inputs[image_param]							).latent_dist.mode()

         UpperCAmelCase_           =pipe(**_lowerCAmelCase							)[0]

         UpperCAmelCase_           =np.abs(out - out_latents_inputs							).max()
         self.assertLess(_lowerCAmelCase		,	1e-4		,	"passing latents as image input generate different result from passing image"							)

@slow
@require_torch_gpu
class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Optional[int]:
         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    Tuple=0							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =torch.manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           =load_image(
             "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"							)
         UpperCAmelCase_           ={
             "prompt": "turn him into a cyborg",
             "image": image,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "image_guidance_scale": 1.0,
             "output_type": "numpy",
         }
         return inputs



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline.from_pretrained(
             "timbrooks/instruct-pix2pix"		,	safety_checker=_lowerCAmelCase							)
         pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =self.get_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]							)

         assert np.abs(expected_slice - image_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline.from_pretrained(
             "timbrooks/instruct-pix2pix"		,	safety_checker=_lowerCAmelCase							)
         UpperCAmelCase_           =LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =self.get_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]							)

         assert np.abs(expected_slice - image_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline.from_pretrained(
             "timbrooks/instruct-pix2pix"		,	safety_checker=_lowerCAmelCase							)
         UpperCAmelCase_           =DDIMScheduler.from_config(pipe.scheduler.config							)
         pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =self.get_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							).images
         UpperCAmelCase_           =image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]							)

         assert np.abs(expected_slice - image_slice							).max() < 1e-3



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =0

         def callback_fn(_lowerCAmelCase:    int		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    torch.FloatTensor							) -> None:
            UpperCAmelCase_           =True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
               UpperCAmelCase_           =latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCAmelCase_           =latents[0, -3:, -3:, -1]
               UpperCAmelCase_           =np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]							)

               assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
            elif step == 2:
               UpperCAmelCase_           =latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCAmelCase_           =latents[0, -3:, -3:, -1]
               UpperCAmelCase_           =np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]							)

               assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

         UpperCAmelCase_           =False

         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline.from_pretrained(
             "timbrooks/instruct-pix2pix"		,	safety_checker=_lowerCAmelCase		,	torch_dtype=torch.floataa							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =self.get_inputs()
         pipe(**_lowerCAmelCase		,	callback=_lowerCAmelCase		,	callback_steps=1							)
         assert callback_fn.has_been_called
         assert number_of_steps == 3



      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Dict:
         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline.from_pretrained(
             "timbrooks/instruct-pix2pix"		,	safety_checker=_lowerCAmelCase		,	torch_dtype=torch.floataa							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)
         pipe.enable_attention_slicing(1							)
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase_           =self.get_inputs()
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							)

         UpperCAmelCase_           =torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9






      def 						lowerCAmelCase__     (						self:    Dict							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         UpperCAmelCase_           =inputs["image"].resize((504, 504)							)

         UpperCAmelCase_           ="timbrooks/instruct-pix2pix"
         UpperCAmelCase_           =StableDiffusionInstructPixaPixPipeline.from_pretrained(
             _lowerCAmelCase		,	safety_checker=_lowerCAmelCase		,	)
         pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =pipe(**_lowerCAmelCase							)
         UpperCAmelCase_           =output.images[0]

         UpperCAmelCase_           =image[255:258, 383:386, -1]

         assert image.shape == (504, 504, 3)
         UpperCAmelCase_           =np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 5e-3












"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()